Generators (PaletteViz Plots)¶
Notebook for generating basic radviz plots of the Pareto-optimal fronts with appropriate colorings and point-sizes.¶
This notebook can be used to generate basic PaletteViz plots of all the Pareto-optimal fronts with suitable colorings and point-sizes. This notebook tests the plot() fucntion from the vis.plotting.paletteviz module. All the Pareto-optimal data point files hard-coded in the dictionary pfs. Currently this notebook provides these Pareto-optimal fronts.
- DTLZ2 ($m$-Sphere) Problem
- DEBMDK (Knee) Problem
- CDEBMDK (Constrained Knee) Problem
- C0-DTLZ2 (A split $m$-sphere with a small isolated cluster at $f_m$-axis)
- C2-DTLZ2 Problem
- DTLZ8 Problem (A 3-dimensional line and an $m$-dimensional hypersurface)
- GAA Problem (A 10-objective and 18-constraint general aviation design problem)
In [1]:
%reload_ext autoreload
%autoreload 2
import sys
import os
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
plt.rcParams.update({'figure.max_open_warning': 0})
Generate and save plots.¶
In [2]:
sys.path.append('../')
from vis.plotting.utils import resize_by_tradeoff, color_by_dist, color_by_cv, enhance_color
def load_props(path):
r"""
Load CV, Mu, Ik and S from path.
"""
CV = None
cvpathf = os.path.join(path, "datacv.csv")
if os.path.exists(cvpathf):
CV = np.loadtxt(cvpathf)
# load the Mu values
Mu = None
mupathf = os.path.join(path, "mu.csv")
if os.path.exists(mupathf):
Mu = np.loadtxt(mupathf)
# load the knee indices
Ik = None
kpathf = os.path.join(path, "muid.csv")
if os.path.exists(kpathf):
Ik = np.loadtxt(kpathf, dtype=int)
return CV, Mu, Ik
def decide_size_color(CV, Mu, Ik):
r"""
Decide point-size and color from `CV`, `Mu` and `Ik`
"""
# decide point-size
S = 1
if Mu is not None and Ik is not None:
S = resize_by_tradeoff(Mu, k=Ik)
# decide colors
# C = default_color(F.shape[0], alpha=0.5)
M = np.mean(F, axis=0)
C,D = color_by_dist(F, M)
# if CV is available, use CV to color
if CV is not None:
C = color_by_cv(CV)
# enhance knee points
if Ik is not None:
C = enhance_color(C, Ik)
return S, C, D
Plot PaletteViz using Star-coordinates¶
In [3]:
%matplotlib inline
sys.path.append('../')
from vis.plotting import paletteviz
from vis.plotting.paletteviz import camera_angles_star
pfs = {'dtlz2': ['3d', '4d', '8d'], \
'dtlz2-nbi': ['3d', '4d', '8d'], \
'debmdk': ['3d', '4d', '8d'], \
'debmdk-nbi': ['3d', '4d', '8d'], \
'debmdk-all': ['3d', '4d', '8d'], \
'debmdk-all-nbi': ['3d', '4d', '8d'], \
'dtlz8': ['3d', '4d', '6d', '8d'], \
'dtlz8-nbi': ['3d', '4d', '6d', '8d'], \
'c2dtlz2': ['3d', '4d', '5d', '8d'], \
'c2dtlz2-nbi': ['3d', '4d', '5d', '8d'], \
'cdebmdk': ['3d', '4d', '8d'], \
'cdebmdk-nbi': ['3d', '4d', '8d'], \
'c0dtlz2': ['3d', '4d', '8d'], \
'c0dtlz2-nbi': ['3d', '4d', '8d'], \
'crash-nbi': ['3d'], 'crash-c1-nbi': ['3d'], 'crash-c2-nbi': ['3d'], \
'gaa': ['10d'], \
'gaa-nbi': ['10d']}
constrained_pfs = ['dtlz8', 'dtlz8-nbi', 'c2dtlz2', 'c2dtlz2-nbi', 'cdebmdk', 'cdebmdk-nbi', \
'c0dtlz2', 'c0dtlz2-nbi', 'gaa', 'gaa-nbi']
for pf in list(pfs.keys()):
for dim in pfs[pf]:
fullpathf = "../data/{0:s}/{1:s}/dataf.csv".format(pf, dim)
if os.path.exists(fullpathf):
path, filenamef = os.path.split(fullpathf)
dirs = path.split('/')
frontname = dirs[-2]
# load the front
F = np.loadtxt(fullpathf, delimiter=',')
print(fullpathf, F.shape, dirs, frontname)
# decide color and sizes
CV, Mu, Ik = load_props(path)
S,C,D = decide_size_color(CV, Mu, Ik)
# make indices for knee and non-knee points
I = np.zeros(F.shape[0]).astype(bool)
I[Ik] = True
Ip, Ik = ~I, I
# fix point labels
L = np.array(["" for _ in range(F.shape[0])]).astype(object)
L[Ip] = "All Other Points"
L[Ik] = "Knee Points"
# decide what label and gradient to use for the colorbar
if pf in constrained_pfs:
cbl = "Normalized Cumulative CV"
cbg = CV
else:
cbl = "Centroid Distance"
cbg = D
figpath = os.path.join(path, "palette-starviz.pdf")
depth_contour_path = os.path.join(path, "depth-cont-cvhull.csv")
with plt.rc_context({"text.usetex": True, "font.size": 12}):
# plot
ax = Axes3D(plt.figure())
# a more elaborate example
paletteviz.plot(F, ax=ax, depth_contours=depth_contour_path, mode='star', \
n_partitions=4, s=S, c=C, labels=L, euler=camera_angles_star[pf][dim], \
colorbar=(C[Ip], cbg[Ip], cbl), verbose=True)
# a minimal example
# paletteviz.plot(F, ax=ax, depth_contours=depth_contour_path, mode='star', n_partitions=4, \
# s=S, c=C, euler=camera_angles_star[pf][dim], verbose=True)
# try to remove white space as much as possible
plt.subplots_adjust(top=0.01, bottom=0, right=0.01, left=0, hspace=0, wspace=0)
plt.margins(0,0,0)
plt.gca().xaxis.set_major_locator(plt.NullLocator())
plt.gca().yaxis.set_major_locator(plt.NullLocator())
plt.gca().zaxis.set_major_locator(plt.NullLocator())
# save the fig
plt.savefig(figpath, bbox_inches='tight', dpi=150, pad_inches=0)
# show
plt.show()
Plot PaletteViz using Radviz¶
In [4]:
%matplotlib inline
sys.path.append('../')
from vis.plotting import paletteviz
from vis.plotting.paletteviz import camera_angles_radviz
pfs = {'dtlz2': ['3d', '4d', '8d'], \
'dtlz2-nbi': ['3d', '4d', '8d'], \
'debmdk': ['3d', '4d', '8d'], \
'debmdk-nbi': ['3d', '4d', '8d'], \
'debmdk-all': ['3d', '4d', '8d'], \
'debmdk-all-nbi': ['3d', '4d', '8d'], \
'dtlz8': ['3d', '4d', '6d', '8d'], \
'dtlz8-nbi': ['3d', '4d', '6d', '8d'], \
'c2dtlz2': ['3d', '4d', '5d', '8d'], \
'c2dtlz2-nbi': ['3d', '4d', '5d', '8d'], \
'cdebmdk': ['3d', '4d', '8d'], \
'cdebmdk-nbi': ['3d', '4d', '8d'], \
'c0dtlz2': ['3d', '4d', '8d'], \
'c0dtlz2-nbi': ['3d', '4d', '8d'], \
'crash-nbi': ['3d'], 'crash-c1-nbi': ['3d'], 'crash-c2-nbi': ['3d'], \
'gaa': ['10d'], \
'gaa-nbi': ['10d']}
constrained_pfs = ['dtlz8', 'dtlz8-nbi', 'c2dtlz2', 'c2dtlz2-nbi', 'cdebmdk', 'cdebmdk-nbi', \
'c0dtlz2', 'c0dtlz2-nbi', 'gaa', 'gaa-nbi']
for pf in list(pfs.keys()):
for dim in pfs[pf]:
fullpathf = "../data/{0:s}/{1:s}/dataf.csv".format(pf, dim)
if os.path.exists(fullpathf):
path, filenamef = os.path.split(fullpathf)
dirs = path.split('/')
frontname = dirs[-2]
# load the front
F = np.loadtxt(fullpathf, delimiter=',')
print(fullpathf, F.shape, dirs, frontname)
# decide color and sizes
CV, Mu, Ik = load_props(path)
S,C,D = decide_size_color(CV, Mu, Ik)
# make indices for knee and non-knee points
I = np.zeros(F.shape[0]).astype(bool)
I[Ik] = True
Ip, Ik = ~I, I
# fix point labels
L = np.array(["" for _ in range(F.shape[0])]).astype(object)
L[Ip] = "All Other Points"
L[Ik] = "Knee Points"
# decide what label and gradient to use for the colorbar
if pf in constrained_pfs:
cbl = "Normalized Cumulative CV"
cbg = CV
else:
cbl = "Centroid Distance"
cbg = D
figpath = os.path.join(path, "palette-radviz.pdf")
depth_contour_path = os.path.join(path, "depth-cont-cvhull.csv")
with plt.rc_context({"text.usetex": True, "font.size": 12}):
# plot
ax = Axes3D(plt.figure())
# paletteviz.plot(F, ax=ax, depth_contours=depth_contour_path, mode='radviz', \
# n_partitions=4, s=S, c=C, labels=L, euler=camera_angles_radviz[pf][dim], \
# colorbar=(C[Ip], cbg[Ip], cbl), verbose=True)
# minimal example
paletteviz.plot(F, ax=ax, depth_contours=depth_contour_path, mode='star', n_partitions=4, \
s=S, c=C, euler=camera_angles_star[pf][dim], verbose=True)
# try to remove white space as much as possible
plt.subplots_adjust(top=0.01, bottom=0, right=0.01, left=0, hspace=0, wspace=0)
plt.margins(0,0,0)
plt.gca().xaxis.set_major_locator(plt.NullLocator())
plt.gca().yaxis.set_major_locator(plt.NullLocator())
plt.gca().zaxis.set_major_locator(plt.NullLocator())
# save the fig
plt.savefig(figpath, bbox_inches='tight', dpi=150, pad_inches=0)
# show
plt.show()
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